Curvelet 域蒙特卡罗估计的随机噪声衰减

针对低信噪比地震资料进行噪声压制时， 传统滤波方法容易损伤有效波。研究认为， curvelet 多尺度多方向的分析能力可以有效分离随机噪声， 提出基于蒙特卡罗估计的自适应非线性阈值函数法衰减噪声能量， 实现在压制噪声的同时保持有效反射信息。模型算例及大巴山地区某地震资料的处理实例表明， 该方法能够有效地压制随机干扰， 同相轴连续性与剖面信噪比较传统小波方法显著提高， 一定程度上改善了常规滤波处理方法在压制噪声的同时对有效波的影响。

Seismic Random Noise Attenuation via Monte Carlo Estimator in CurveletDomain

Fortherandomnoisesuppressinginseismicrecordswithlowsignaltonoiseratio， traditionalmethodswillharm the signal components. The paper thinks curvelet transform can separate the random noise using multi-scaleand multi-direction. The authors apply Monte Carlo estimator to compute the noise level and design a non-linearthresholding function to remove the random noise coefficients， so the useful signal will be recovered. Applicationson both synthetic data and actual seismic data from Dabashan area show that the new method eliminates the noiseportion of the signal more efficiently and retains a greater amount of geologic data. The quality and consecutive ofseismic event are better as well as the quality of section is improved obviously， and it overcomes the drawback thatthe conventional filtering approach may affect the effective wave when suppressing noise.